Recent advancements in the field of image processing have led to development of various methods and techniques for detection of objects in motion. Typically, the detection of the objects in motion may be performed based on a global motion vector for each object in motion. The global motion vector may be calculated based on comparison of a current frame with respect to a previous frame. Thereafter, the extraction of the objects in motion may be performed based on subtraction of an image of fitted previous frame from an image of the current frame.
However, in certain scenarios, the calculation of such global motion vector may be incorrect due to various intrinsic and/or extrinsic factors, such as large foreground movement, blur, parallax, illuminance change, and/or roller shutter effect. Accordingly, the objects in motion may not be detected correctly. Further, the detected parts of the objects in motion may not correspond to the actual objects in motion in the image. Consequently, the extraction of the objects in motion in the image may not be accurate. Thus, an advanced, efficient, and accurate image processing system and/or technique may be required for an improved performance of extraction of objects in motion.
Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.